nep-net New Economics Papers
on Network Economics
Issue of 2022‒09‒19
six papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. On the Estimation of Peer Effects for Sampled Networks By Mamadou Yauck
  2. Myerson on a Network By Rangeet Bhattacharyya; Palash Dey; Swaprava Nath
  3. Modeling Path-Dependent State Transition by a Recurrent Neural Network By Yang, Bill Huajian
  4. Generalised Gately Values of Cooperative Games By Robert P. Gilles; Lina Mallozzi
  5. Theory and Evidence of Firm-to-firm Transaction Network Dynamics By KAWAKUBO Takafumi; SUZUKI Takafumi
  6. Patronage and power in rural India: a study based on interaction networks By Anindya Bhattacharya; Anirban Kar; Sunil Kumar; Alita Nandi

  1. By: Mamadou Yauck
    Abstract: This paper deals with the estimation of exogeneous peer effects for partially observed networks under the new inferential paradigm of design identification, which characterizes the missing data challenge arising with sampled networks with the central idea that two full data versions which are topologically compatible with the observed data may give rise to two different probability distributions. We show that peer effects cannot be identified by design when network links between sampled and unsampled units are not observed. Under realistic modeling conditions, and under the assumption that sampled units report on the size of their network of contacts, the asymptotic bias arising from estimating peer effects with incomplete network data is characterized, and a bias-corrected estimator is proposed. The finite sample performance of our methodology is investigated via simulations.
    Date: 2022–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2208.09102&r=
  2. By: Rangeet Bhattacharyya; Palash Dey; Swaprava Nath
    Abstract: The auction of a single indivisible item is one of the most celebrated problems in mechanism design with transfers. Despite its simplicity, it provides arguably the cleanest and most insightful results in the literature. When the information of the auction is available to every participant, Myerson [17] provided a seminal result to characterize the incentive-compatible auctions along with revenue optimality. However, such a result does not hold in an auction on a network, where the information of the auction is spread via the agents, and they need incentives to forward the information. In recent times, a few auctions (e.g., [10, 15]) were designed that appropriately incentivize the intermediate nodes on the network to promulgate the information to potentially more valuable bidders. In this paper, we provide a Myerson-like characterization of incentive-compatible auctions on a network and show that the currently known auctions fall within this larger class of randomized auctions. We obtain the structure of the revenue optimal auction for i.i.d. bidders on arbitrary trees. We discuss the possibilities of addressing more general settings. Through experiments, we show that auctions following this characterization can provide a higher revenue than the currently known auctions on networks.
    Date: 2022–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2208.09326&r=
  3. By: Yang, Bill Huajian
    Abstract: Rating transition models are widely used for credit risk evaluation. It is not uncommon that a time-homogeneous Markov rating migration model deteriorates quickly after projecting repeatedly for a few periods. This is because the time-homogeneous Markov condition is generally not satisfied. For a credit portfolio, rating transition is usually path dependent. In this paper, we propose a recurrent neural network (RNN) model for modeling path-dependent rating migration. An RNN is a type of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. There are neurons for input and output at each time-period. The model is informed by the past behaviours for a loan along the path. Information learned from previous periods propagates to future periods. Experiments show this RNN model is robust.
    Keywords: Path-dependent, rating transition, recurrent neural network, deep learning, Markov property, time-homogeneity
    JEL: C13 C18 C45 C51 C58 G12 G17 G32 G33 M3
    Date: 2022–08–18
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:114188&r=
  4. By: Robert P. Gilles; Lina Mallozzi
    Abstract: We investigate Gately's solution concept for cooperative games with transferable utilities. Gately's solution conception is a bargaining solution and tries to minimise the maximal quantified "propensity to disrupt" the negotiation of the players over the allocation of the generated collective payoffs. We show that Gately's solution concept is well-defined for a broad class of games. We consider a generalisation based on a parameter-based quantification of the propensity to disrupt. Furthermore, we investigate the relationship of Gately's solution and its generalisation with the Core. We show that Gately's solution is in the Core for all regular 3-player games. We also identify precise conditions under which generalised Gately values are Core imputations for arbitrary regular cooperative games. We construct the dual of generalised Gately values and devise an axiomatisation of these values for the class of regular cooperative games. We conclude the paper with an application of the Gately value to the measurement of power in hierarchical social networks.
    Date: 2022–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2208.10189&r=
  5. By: KAWAKUBO Takafumi; SUZUKI Takafumi
    Abstract: How are supply chains formed and restructured over time? This paper investigates firm-to-firm transaction network dynamics from theoretical and empirical perspectives, exploiting large-scale firm-level transaction data from Japan. First, we provide basic facts which show substantial churning in supply chains over time, even after excluding the cases where either supplier or customer firms exit from the market. Second, we empirically find that productivity positive assortative matching between firms exists. Firms are more likely to keep trading with more productive firms and instead stop trading with less productive ones. Alternatively, more productive firms start new transactions with more productive business partners. Lastly, we build a theoretical framework to rationalize these findings. Both supplier and customer firms are heterogeneous and choose their trading partners with a many-to-many matching framework. We derive the implications for supply chain formation and restructuring in response to productivity shocks.
    Date: 2022–08
    URL: http://d.repec.org/n?u=RePEc:eti:dpaper:22073&r=
  6. By: Anindya Bhattacharya; Anirban Kar; Sunil Kumar; Alita Nandi
    Abstract: This work has two intertwined components: first, as part of a research programme it introduces a new methodology for identifying `power-centres' in rural societies of developing countries in general and then applies that in the specific context of contemporary rural India for addressing some debates on the dynamics of power in rural India. We identify the nature of `local' rural institutions based on primary data collected by ourselves (in 2013 and 2014). We took 36 villages in the states of Maharashtra, Odisha and Uttar Pradesh - 12 in each of these states - as the sites for our observation and data collection. We quantify nature of institutions from data on the day-to-day interactions of households in the spheres of economy, society and politics. Our household survey shows that there is substantial variation in power structure across regions. We identified the presence of `local elites' in 22 villages out of 36 surveyed. We conducted a follow-up survey, called `elite survey', to get detailed information about the identified elite households. We observe that landlordism has considerably weakened, land has ceased to be the sole source of power and new power-centres have emerged. Despite these changes, caste, landownership and patron-client relation continue to be three important pillars of rural power structure.
    Date: 2022–08
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2208.05002&r=

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